Could Collected Chemical Parameters Be Utilized to Build Soft Sensors Capable of Predicting the Provenance, Vintages, and Price Points of New Zealand Pinot Noir Wines Simultaneously?

Abstract

Soft sensors work as predictive frameworks encapsulating a set of easy-to-collect input data and a machine learning method (ML) to predict highly related variables that are difficult to measure. The machine learning method could provide a prediction of complex unknown relations between the input data and desired output parameters. Recently, soft sensors have been applicable in predicting the prices and vintages of New Zealand Pinot noir wines based on chemical parameters. However, the previous sample size did not adequately represent the diversity of provenances, vintages, and price points across commercially available New Zealand Pinot noir wines. Consequently, a representative sample of 39 commercially available New Zealand Pinot noir wines from diverse provenances, vintages, and price points were selected. Literature has shown that wine phenolic compounds strongly correlated with wine provenances, vintages and price points, which could be used as input data for developing soft sensors. Due to the significance of these phenolic compounds, chemical parameters, including phenolic compounds and pH, were collected using UV-Vis visible spectrophotometry and a pH meter. The soft sensor utilising Naive Bayes (ML) was designed to predict Pinot noir wines’ provenances (regions of origin) based on six chemical parameters with the prediction accuracy of over 75%. Soft sensors based on decision trees (ML) could predict Pinot noir wines’ vintages and price points with prediction accuracies of over 75% based on six chemical parameters. These predictions were based on the same collected six chemical parameters as aforementioned.

The importance of outlier rejection and significant explanatory variable selection for pinot noir wine soft sensor development

“…Soft sensors work by effectively combining easy-to-collect input data with a machine learning algorithm to predict target variables that are difficult or costly to measure ( An et al, 2023 ). The machine learning method (ML) could provide a prediction of complex unknown relations between the input data and desired output data ( An et al, 2023 ).…”

“…Soft sensors work by effectively combining easy-to-collect input data with a machine learning algorithm to predict target variables that are difficult or costly to measure ( An et al, 2023 ). The machine learning method (ML) could provide a prediction of complex unknown relations between the input data and desired output data ( An et al, 2023 ). For example, when 12 physicochemical data were used, the predicted accuracy of the model using the support vector machine algorithm (belong to ML) on wine quality reached 68.94% ( Kumar et al, 2020 ).…”